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Volumn , Issue , 2009, Pages 1681-1688

Efficient sampling for Gaussian process inference using control variables

Author keywords

[No Author keywords available]

Indexed keywords

DIFFERENTIAL EQUATIONS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); MARKOV PROCESSES; PROCESS CONTROL;

EID: 83855162680     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (39)

References (16)
  • 3
    • 78049443039 scopus 로고    scopus 로고
    • Accelerating Bayesian inference over nonlinear differential equations with Gaussian processes
    • B. Calderhead, M. Girolami, and N.D. Lawrence. Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes. In Neural Information Processing Systems, 22, 2008.
    • (2008) Neural Information Processing Systems , vol.22
    • Calderhead, B.1    Girolami, M.2    Lawrence, N.D.3
  • 4
    • 0038891993 scopus 로고    scopus 로고
    • Sparse online Gaussian processes
    • L. Csato and M. Opper. Sparse online Gaussian processes. Neural Computation, 14:641-668, 2002.
    • (2002) Neural Computation , vol.14 , pp. 641-668
    • Csato, L.1    Opper, M.2
  • 6
    • 25444528713 scopus 로고    scopus 로고
    • Assessing approximate inference for binary Gaussian process classification
    • M. Kuss and C. E. Rasmussen. Assessing Approximate Inference for Binary Gaussian Process Classification. Journal of Machine Learning Research, 6:1679-1704, 2005.
    • (2005) Journal of Machine Learning Research , vol.6 , pp. 1679-1704
    • Kuss, M.1    Rasmussen, C.E.2
  • 9
    • 0345978970 scopus 로고    scopus 로고
    • Expectation propagation for approximate Bayesian inference
    • T. Minka. Expectation propagation for approximate Bayesian inference. In UAI, pages 362-369, 2001.
    • (2001) UAI , pp. 362-369
    • Minka, T.1
  • 10
    • 0004220749 scopus 로고    scopus 로고
    • Monte Carlo implementation of Gaussian process models for Bayesian regression and classification
    • University of Toronto
    • R. M. Neal. Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. Technical report, Dept. of Statistics, University of Toronto, 1997.
    • (1997) Technical Report, Dept. of Statistics
    • Neal, R.M.1
  • 13
    • 34249856850 scopus 로고    scopus 로고
    • Bayesian model-based inference of transcription factor activity
    • S. Rogers, R. Khanin, and M. Girolami. Bayesian model-based inference of transcription factor activity. BMC Bioinformatics, 8(2), 2006.
    • (2006) BMC Bioinformatics , vol.8 , Issue.2
    • Rogers, S.1    Khanin, R.2    Girolami, M.3
  • 14
    • 36748998542 scopus 로고    scopus 로고
    • Approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations
    • H. Rue, S. Martino, and N. Chopin. Approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations. NTNU Statistics Preprint, 2007.
    • (2007) NTNU Statistics Preprint
    • Rue, H.1    Martino, S.2    Chopin, N.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.